The type of workload managed by a computing entity (or “computing system”), e.g., a database management system or a network storage system, is a key consideration in tuning the computing entity. For example, allocation of resources, e.g., main memory, can vary significantly depending on whether the workload type is Online Transaction Processing (OLTP) or Decision Support System (DSS). It would be preferable for administrators of the computing entity, e.g., database administrators or system administrators, to recognize significant shifts in workload types that would require reconfiguring the computing entity, co-locating similar workloads, or taking other actions to maintain acceptable levels of performance.
Currently, the identification of a workload's type is generally performed manually, e.g., by pre-classifying a given workload into a class, for example, “test,” “web server,” or “database,” where the class identifies an expected load pattern and/or behavior. For example, a web server may employ few memory resources but a tremendous amount of network resources. On the other hand, a database may employ a tremendous amount of processing and storage resources. However, human classification of workload type is becoming increasingly difficult as the complexity of workloads increases. Furthermore, the computing entity may execute several workload types in parallel. To analyze these different workloads, administrators may generate reports having details of these workloads and their types. However, the reports are complex, often contain too much information, and are not typically very user-friendly. Consequently, it is a cumbersome task for an administrator to read and analyze the reports. Accordingly, current workload identification techniques are limited in their capabilities and usefulness.